Data Architect vs. Data Engineer: Differences and Details

As organizations evolve to meet the technological demands of the future, digital infrastructure continues to become more complex. Data architects and data engineers have ongoing responsibilities to develop, monitor, and improve these information technology systems. These roles are especially important in a world filled with artificial intelligence (AI) applications and machine learning (ML) capabilities, where data is a critical driving factor for success.

Generally, data architects set the structures and processes for data-related activities while data engineers build on that infrastructure. Engaging the appropriate resources to satisfy this important distinction allows organizations to manage and optimize data-related projects. Through the use of AI, architects and engineers will be able to quickly identify patterns and trends represented in data in a way that generates deeper insights and improves the decision-making process.

This article will look at some of the key features of each role as well as how they'll likely develop in this emerging world with transformation driven by artificial intelligence and machine learning.

Is a data architect the same as a data engineer?

While data architects and data engineers see their skill sets overlap in some areas (as with many data and computer science roles), they fulfill separate roles on a data management team.

Data architects are primarily responsible for designing database systems architecture, providing the framework for how data is accessed, stored, and managed. In other words, they must analyze the needs of stakeholders, review data flows and requirements, and create physical designs for databases. In addition, they build effective infrastructure that allows for efficient analysis of large-scale datasets. In the hands of data scientists, these data sets enable infrastructure to develop rapidly and test ML models.

Other duties that define the job description of a data architect include:

• Collaborating with stakeholders to understand their needs and requirements
• Determining the overall data architecture for an organization, including the data model, data storage, and data security systems to set in place
• Monitoring and maintaining the data architecture to ensure that it meets the business's needs

On the other hand, data engineers create data pipelines and refine the existing architecture to ensure the most efficient use of data. Their duties include designing, developing, and testing data pipelines needed to power machine learning models. To achieve that, they process data to prepare it for training and monitor the performance of the models.

Other duties associated with data engineers include:

• Data processing and organizing large data sets for organizational use
• Creating algorithms to analyze data
• Maintaining the data infrastructure and scaling it to handle growing company data
• Building a system for automating data analysis and management processes

The table below summarizes the key differences between the roles of a data architect vs data engineer:

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What does a data architect do

In the same way that a construction architect creates the blueprints for a building, a data architect designs the plans for your data framework. Data architects have to pay attention to the big picture of the network. Because of this, they have to master many different data management skills and must have a complex understanding of a wide array of non-relational databases.

For starters, they will need to develop an effective approach to digital organization using the integration of networked architecture and data services. Commonly referred to as a data fabric, this system provides full visibility and control over all of an organization’s data without compromising security or accessibility.

They also must know how to develop a modern data architecture that supports their company’s gravitation toward AI initiatives and transformation. Failure to build the necessary infrastructure can create quality issues and limit the capability of the new system. Using a well-researched framework or approach can cut costs of implementation and improve the marketability and reusability of your organization’s AI initiatives.

The non-relational databases mentioned above are also known as NoSQL databases. SQL stands for “structured query language” and is the primary programming language used in relational databases. In addition to understanding NoSQL and SQL, a data architect should also have an expert-level understanding of other programming languages, like Python, Java, and PHP.

The most basic type of non-relational database is a key-value model in which information is stored into keys and values. Data architects should be familiar with these types of databases and with several other non-relational database types, including column store, document, and graph databases.

Knowing how document-understanding AI models work is also beneficial, as it eases the workflow for recognizing and extracting data like passports, specific text and tables, receipts, and IDs from documents stored in databases.

The data architect is like the manager of a data project and also needs adept communication skills to share their vision with the other members of their team. They have to be able to interact with data engineers and data science professionals so that the team can use the database effectively.

When you’re interviewing data architects, be sure to ask what data tools they have experience with, what programming languages they feel most comfortable with, what data projects they’ve helmed in the past, and how they’re dealing with data integration and cloud infrastructure. Remember that a data architect will be the head of your data management team and someone you should feel comfortable giving the reins.

A data architect has these specific responsibilities.

Develop a thorough understanding of client data needs

A data architect must maintain open communication with a client and have a crystal-clear understanding of a company’s informational needs. The data architect has to know what data has to be collected and how it will be used. 

With this information, the architect can identify various data sources relevant for the client’s project and design methods for extracting and managing the database to meet all of a business’s expectations.

Ensure database functionality and security

A data architect is responsible for maintaining a database’s functionality. Typical procedures include creating and testing various model designs, setting database development standards that will guide the data engineers, and managing the data warehouses and analytics systems. 

Data architects perform quality tests to make sure the database is still operating efficiently and meeting its required purpose. They use AI-powered automated testing tools to run frequent tests and catch any anomaly early. If the test result signals any anomaly, the architect sets to work on updating the database structure.

Train other team members to use the database effectively

To make sure everyone’s on the same page, the data architect is often tasked with teaching the members of the data team to use the database effectively. It’s up to the architect to answer any questions the team may have about the data framework. Data engineers and data scientists will look to the data architect to quickly solve any problems they come across.

Communicate effectively on why the model works

Company executives need to know that a database will work for them. As their head point of contact, a data architect must create reports demonstrating how their database will perform and why it’s the right model. That’s why it’s important for data architects to possess great communication skills in addition to technical skills.

Ensure compliance with data privacy and usage regulations

A data architect determines who accesses the data, how they access it, and what they use it for. They formulate a data governance policy within an organization that defines the best practices to ensure data privacy. Who is responsible for the data at different stages and how they should or should not use the data are some core aspects of the policy document.

What data engineers do

A data engineer is responsible for building and maintaining the infrastructure that collects, stores, and processes data. They collect data from data warehouses and various other data sources and clean and transform it before building and maintaining data pipelines.

Like skilled construction workers who connect electrical and plumbing lines, data engineers connect data pipelines that carry information. They work with various AI technologies such as simple rulesets that can standardize data across the entire system. They may also implement AI models that can predict future events based on past data as well as more complex models that can extract high-level data from past insights. Finally, they can utilize cloud computing data platforms and data manipulation techniques to carry out data mining, data warehousing cleaning, virtualization, and automation.

The workflow for data engineers typically follows this cycle:

• Collect data from a variety of sources, including databases, files, and sensors
• Clean and transform the data to make it ready for analysis
• Build and maintain data pipelines that move data between different systems
• Develop and implement new data validation tools and applications so data analysts and scientists can effectively draw inferences from data
• Provide technical support to data analysts, data scientists, software engineers, business analysts, and other data professionals

Considering the demands of their roles, job owners typically demand that data engineers possess a high understanding of complex technical concepts and how databases work. As a result, a data engineer should have:

• Experience with relational and non-relational databases, including MySQL, Oracle, and SQL Server, MongoDB, Cassandra, and Hadoop)
• Proficiency with cloud platforms including Google Cloud, Microsoft Azure, and Amazon Web Services (AWS)
• A good knowledge of analytics tools and how they work to get insights from data and visualize their results
• Proper communication skills, alongside other soft skills, to collaborate with stakeholders and understand their points of interest.
• An ability to use generative AI models throughout the development process to quickly generate code, automate certain tasks, and enhance productivity.
• A knowledge of additional AI capabilities such as training the system to fix errors, make recommendations, remove redundancy, and fill in missing values based on context clues.

A data engineer has these specific responsibilities.

Mine data for the database

Data engineers take the database framework designed by the architect and fill it in. To do this, they must have a solid understanding of an architect’s model. Engineers use data mining techniques to extract the necessary data for a database. Data mining has to do with the process of searching through a large base of raw data to identify patterns and trends. Through the implementation of data science and complex models, AI can help streamline this process. Once implemented, data engineers can make sure data pipelines are connected so information can be accessed as efficiently as possible.

Create reports based on the data

Engineers work with data analysts and scientists. Data scientists will tell data engineers what type of data they need so that the engineer can create a report. In some cases, data engineers can assume the duties of business intelligence developers to create tools that help the scientist generate reports and visually present specific data. The engineer must create their report so that it’s easy for a scientist to understand and evaluate. They may use generative AI to help with creating written and visual content.

Optimize operational processes

Data engineers are always looking for ways to improve data-gathering tasks. With a keen eye, they comb through their operational processes to see if any jobs can be optimized. They then implement changes if anything can be automated. AI tools like web scraping, or using AI to quickly extract data from websites, makes this even faster.

Test data pipelines

Data engineers, with data architects and DevOps experts, consistently monitor and test the performance and stability of the data pipelines. In case of any errors, the engineer spots the problem and finds an appropriate solution. Data engineers also frequently update automated pipelines periodically as business needs change.

Deploy machine learning models

While data scientists create the machine learning models, data engineers deploy these models in production environments when a machine learning engineer is not available. Data engineers also connect to a data source or data warehouse and create tools for monitoring the model’s performance. This starts with defining the problem you hope to solve and gathering the necessary data to support the development of your tool.

Build your data management team with Upwork

Now that big data, machine learning, and artificial intelligence have greatly expanded organizations’ access to information regarding their consumers, the need to put together quality database management teams has become essential. However, finding the right people for your team can be difficult and time-consuming. Remote talent platforms like Upwork provide an efficient and cost-effective way to choose skilled and independent workers from a huge talent pool.

In addition to finding part-time professionals on Upwork for smaller or shorter-term projects, you can also hire full-time experts for your data management team. If you’re looking to fill open positions in your data team, visit Upwork’s Talent Marketplace to find top freelance data architects and data engineers for your big management data team. While some may have a bachelor’s or master’s degree in computer science, you can also find skilled talents with certifications, projects, and years of experience to prove their quality.

Having a strong team helps you gain insights vital for understanding and marketing to your customers with precision.

Data professionals can also visit Upwork to connect with employers and find data architect roles and data engineer roles.